#setwd('/afs/inf.ed.ac.uk/user/s17/s1725186/Documents/PhD-Models/FirstPUModel/RMarkdowns')
library(tidyverse) ; library(reshape2) ; library(glue) ; library(plotly) ; library(dendextend)
library(RColorBrewer) ; library(viridis) ; require(gridExtra) ; library(GGally)
library(expss)
library(polycor)
library(foreach) ; library(doParallel)
library(knitr)
library(biomaRt)
library(anRichment) ; library(BrainDiseaseCollection)
suppressWarnings(suppressMessages(library(WGCNA)))
SFARI_colour_hue = function(r) {
pal = c('#FF7631','#FFB100','#E8E328','#8CC83F','#62CCA6','#59B9C9','#b3b3b3','#808080','gray','#d9d9d9')[r]
}
Load preprocessed dataset (preprocessing code in 19_10_14_data_preprocessing.Rmd) and clustering (pipeline in 19_10_21_WGCNA.Rmd)
# Gandal dataset
load('./../Data/preprocessed_data.RData')
datExpr = datExpr %>% data.frame
DE_info = DE_info %>% data.frame
# GO Neuronal annotations: regex 'neuron' in GO functional annotations and label the genes that make a match as neuronal
GO_annotations = read.csv('./../Data/genes_GO_annotations.csv')
GO_neuronal = GO_annotations %>% filter(grepl('neuron', go_term)) %>%
mutate('ID'=as.character(ensembl_gene_id)) %>%
dplyr::select(-ensembl_gene_id) %>% distinct(ID) %>%
mutate('Neuronal'=1)
# SFARI Genes
SFARI_genes = read_csv('./../../../SFARI/Data/SFARI_genes_08-29-2019_w_ensembl_IDs.csv')
SFARI_genes = SFARI_genes[!duplicated(SFARI_genes$ID) & !is.na(SFARI_genes$ID),]
# Clusterings
clusterings = read_csv('./../Data/clusters.csv')
# Update DE_info with SFARI and Neuronal information
genes_info = DE_info %>% mutate('ID'=rownames(.)) %>% left_join(SFARI_genes, by='ID') %>%
mutate(`gene-score`=ifelse(is.na(`gene-score`), 'None', `gene-score`)) %>%
left_join(GO_neuronal, by='ID') %>% left_join(clusterings, by='ID') %>%
mutate(Neuronal=ifelse(is.na(Neuronal), 0, Neuronal)) %>%
mutate(gene.score=ifelse(`gene-score`=='None' & Neuronal==1, 'Neuronal', `gene-score`),
significant=padj<0.05 & !is.na(padj))
# Add gene symbol
getinfo = c('ensembl_gene_id','external_gene_id')
mart = useMart(biomart='ENSEMBL_MART_ENSEMBL', dataset='hsapiens_gene_ensembl',
host='feb2014.archive.ensembl.org') ## Gencode v19
gene_names = getBM(attributes=getinfo, filters=c('ensembl_gene_id'), values=genes_info$ID, mart=mart)
genes_info = genes_info %>% left_join(gene_names, by=c('ID'='ensembl_gene_id'))
clustering_selected = 'DynamicHybrid'
genes_info$Module = genes_info[,clustering_selected]
dataset = read.csv(paste0('./../Data/dataset_', clustering_selected, '.csv'))
dataset$Module = dataset[,clustering_selected]
rm(DE_info, GO_annotations, clusterings, getinfo, mart, dds)
Using the hetcor function, that calculates Pearson, polyserial or polychoric correlations depending on the type of variables involved.
datTraits = datMeta %>% dplyr::select(Diagnosis, Region, Sex, Age, PMI, RNAExtractionBatch) %>%
dplyr::rename('ExtractionBatch' = RNAExtractionBatch)
# Recalculate MEs with color labels
ME_object = datExpr %>% t %>% moduleEigengenes(colors = genes_info$Module)
MEs = orderMEs(ME_object$eigengenes)
# Calculate correlation between eigengenes and the traits and their p-values
moduleTraitCor = MEs %>% apply(2, function(x) hetcor(x, datTraits)$correlations[1,-1]) %>% t
rownames(moduleTraitCor) = colnames(MEs)
colnames(moduleTraitCor) = colnames(datTraits)
moduleTraitPvalue = corPvalueStudent(moduleTraitCor, nrow(datExpr))
# Create text matrix for the Heatmap
textMatrix = paste0(signif(moduleTraitCor, 2), ' (', signif(moduleTraitPvalue, 1), ')')
dim(textMatrix) = dim(moduleTraitCor)
# In case there are any NAs
if(sum(!complete.cases(moduleTraitCor))>0){
print(paste0(sum(is.na(moduleTraitCor)),' correlation(s) could not be calculated'))
}
## [1] "1 correlation(s) could not be calculated"
rm(ME_object)
Note: The correlation between Module #F464E5 and Diagonsis is the one that cannot be calculated, weirdly enough, the thing that causes the error is that the initial correlation is too high, so it would be a very bad thing to lose this module because of this numerical error. I’m going to fill in its value using the polyserial function, which doesn’t give exactly the same results as the hetcor() function, but it’s quite similar.
# Calculate the correlation tha failed with hetcor()
moduleTraitCor['ME#F464E5','Diagnosis'] = polyserial(MEs[,'ME#F464E5'], datTraits$Diagnosis)
## Warning in polyserial(MEs[, "ME#F464E5"], datTraits$Diagnosis): initial
## correlation inadmissible, 1.01199040044846, set to 0.9999
I’m going to select all the modules that have an absolute correlation higher than 0.9 with Diagnosis to study them
# Sort moduleTraitCor by Diagnosis
moduleTraitCor = moduleTraitCor[order(moduleTraitCor[,1], decreasing=TRUE),]
moduleTraitPvalue = moduleTraitPvalue[order(moduleTraitCor[,1], decreasing=TRUE),]
# Create text matrix for the Heatmap
textMatrix = paste0(signif(moduleTraitCor, 2), ' (', signif(moduleTraitPvalue, 1), ')')
dim(textMatrix) = dim(moduleTraitCor)
labeledHeatmap(Matrix = moduleTraitCor, xLabels = names(datTraits), yLabels = gsub('ME','',rownames(moduleTraitCor)),
yColorWidth=0, colors = brewer.pal(11,'PiYG'), bg.lab.y = gsub('ME','',rownames(moduleTraitCor)),
textMatrix = textMatrix, setStdMargins = FALSE, cex.text = 0.8, cex.lab.y = 0.75, zlim = c(-1,1),
main = paste('Module-Trait relationships'))
diagnosis_cor = data.frame('Module' = gsub('ME','',rownames(moduleTraitCor)),
'MTcor' = moduleTraitCor[,'Diagnosis'],
'MTpval' = moduleTraitPvalue[,'Diagnosis'])
genes_info = genes_info %>% left_join(diagnosis_cor, by='Module')
rm(moduleTraitPvalue, datTraits, textMatrix, diagnosis_cor)
The modules consist mainly of points with very high (absolute) values in PC2 (which we know is related to lfc), so this result is consistent with the high correlation between Module and Diagnosis, although some of the points with the highest PC2 values do not belong to these top modules
top_modules = gsub('ME','',rownames(moduleTraitCor)[abs(moduleTraitCor[,'Diagnosis'])>0.9])
cat(paste0('Top modules selected: ', paste(top_modules, collapse=', '),'\n'))
## Top modules selected: #F464E5, #00BECA
pca = datExpr %>% prcomp
plot_data = data.frame('ID'=rownames(datExpr), 'PC1' = pca$x[,1], 'PC2' = pca$x[,2]) %>%
left_join(dataset, by='ID') %>% left_join(genes_info %>% dplyr::select(ID, external_gene_id), by='ID') %>%
dplyr::select(ID, external_gene_id, PC1, PC2, Module, gene.score) %>%
mutate(ImportantModules = ifelse(Module %in% top_modules, as.character(Module), 'Others')) %>%
mutate(color = ifelse(ImportantModules=='Others','gray',ImportantModules),
alpha = ifelse(ImportantModules=='Others', 0.2, 0.4),
gene_id = paste0(ID, ' (', external_gene_id, ')'))
table(plot_data$ImportantModules)
##
## #00BECA #F464E5 Others
## 1638 626 13884
ggplotly(plot_data %>% ggplot(aes(PC1, PC2, color=ImportantModules)) +
geom_point(alpha=plot_data$alpha, color=plot_data$color, aes(ID=gene_id)) + theme_minimal() +
ggtitle('Modules with strongest relation to Diagnosis'))
rm(pca)
create_plot = function(module){
plot_data = dataset %>% dplyr::select(ID, paste0('MM.',gsub('#','',module)), GS, gene.score) %>% filter(dataset$Module==module)
colnames(plot_data)[2] = 'Module'
SFARI_colors = as.numeric(names(table(as.character(plot_data$gene.score)[plot_data$gene.score!='None'])))
p = ggplotly(plot_data %>% ggplot(aes(Module, GS, color=gene.score)) + geom_point(alpha=0.5, aes(ID=ID)) + ylab('Gene Significance') +
scale_color_manual(values=SFARI_colour_hue(r=c(SFARI_colors,8))) + theme_minimal() + xlab('Module Membership') +
ggtitle(paste0('Module ', module,' (MTcor = ', round(moduleTraitCor[paste0('ME',module),1],2),')')))
return(p)
}
create_plot(top_modules[1])
create_plot(top_modules[2])
rm(create_plot)
List of SFARI Genes in top modules ordered by SFARI score and Gene Significance
table_data = dataset %>% left_join(genes_info %>% dplyr::select(ID, external_gene_id), by='ID') %>%
dplyr::select(ID, external_gene_id, GS, gene.score, Module) %>% arrange(gene.score, desc(abs(GS))) %>%
dplyr::rename('Ensembl ID'=ID, 'Gene Symbol'=external_gene_id,
'SFARI score'=gene.score, 'Gene Significance'=GS)
kable(table_data %>% filter(Module == top_modules[1] & `SFARI score` != 'None') %>% dplyr::select(-Module),
caption=paste0('SFARI Genes for Module ', top_modules[1]))
| Ensembl ID | Gene Symbol | Gene Significance | SFARI score |
|---|---|---|---|
| ENSG00000038382 | TRIO | 0.6264110 | 2 |
| ENSG00000168769 | TET2 | 0.8178650 | 3 |
| ENSG00000083168 | KAT6A | 0.5998865 | 3 |
| ENSG00000113742 | CPEB4 | 0.3701387 | 3 |
| ENSG00000145020 | AMT | 0.3424890 | 3 |
| ENSG00000132510 | KDM6B | 0.3330706 | 3 |
| ENSG00000128573 | FOXP2 | 0.2489053 | 3 |
| ENSG00000112902 | SEMA5A | 0.2191666 | 3 |
| ENSG00000165995 | CACNB2 | 0.1701529 | 3 |
| ENSG00000137801 | THBS1 | 0.8076180 | 4 |
| ENSG00000106366 | SERPINE1 | 0.7907461 | 4 |
| ENSG00000011422 | PLAUR | 0.7753875 | 4 |
| ENSG00000172554 | SNTG2 | 0.7354320 | 4 |
| ENSG00000181481 | RNF135 | 0.6768553 | 4 |
| ENSG00000152208 | GRID2 | 0.6205857 | 4 |
| ENSG00000227184 | EPPK1 | 0.5946476 | 4 |
| ENSG00000183098 | GPC6 | 0.5839280 | 4 |
| ENSG00000188785 | ZNF548 | 0.5801457 | 4 |
| ENSG00000198589 | LRBA | 0.5579943 | 4 |
| ENSG00000182372 | CLN8 | 0.3972145 | 4 |
| ENSG00000071246 | VASH1 | 0.3828434 | 4 |
| ENSG00000196277 | GRM7 | 0.3773957 | 4 |
| ENSG00000239389 | PCDHA13 | 0.3419447 | 4 |
| ENSG00000007314 | SCN4A | 0.3327403 | 4 |
| ENSG00000182472 | CAPN12 | 0.3178125 | 4 |
| ENSG00000224389 | C4B | 0.3077509 | 4 |
| ENSG00000215045 | GRID2IP | 0.2888427 | 4 |
| ENSG00000130508 | PXDN | 0.1235447 | 4 |
| ENSG00000187957 | DNER | 0.0565791 | 4 |
| ENSG00000100024 | UPB1 | 0.3133084 | 5 |
| ENSG00000026508 | CD44 | 0.2637995 | 5 |
| ENSG00000136244 | IL6 | 0.1960508 | 5 |
| ENSG00000184588 | PDE4B | 0.1947772 | 5 |
| ENSG00000141526 | SLC16A3 | 0.8254828 | 6 |
| ENSG00000072364 | AFF4 | 0.7711840 | 6 |
| ENSG00000171791 | BCL2 | 0.3611981 | 6 |
kable(table_data %>% filter(Module == top_modules[2] & `SFARI score` != 'None') %>% dplyr::select(-Module),
caption=paste0('SFARI Genes for Module ', top_modules[2]))
| Ensembl ID | Gene Symbol | Gene Significance | SFARI score |
|---|---|---|---|
| ENSG00000136535 | TBR1 | -0.6544304 | 1 |
| ENSG00000136531 | SCN2A | -0.5379355 | 1 |
| ENSG00000174469 | CNTNAP2 | -0.7002325 | 2 |
| ENSG00000061676 | NCKAP1 | -0.6918489 | 2 |
| ENSG00000139613 | SMARCC2 | -0.4332840 | 2 |
| ENSG00000196557 | CACNA1H | -0.4139346 | 2 |
| ENSG00000119866 | BCL11A | -0.3842245 | 2 |
| ENSG00000157445 | CACNA2D3 | -0.3391182 | 2 |
| ENSG00000144619 | CNTN4 | -0.3111072 | 2 |
| ENSG00000074590 | NUAK1 | -0.8234159 | 3 |
| ENSG00000144285 | SCN1A | -0.8004590 | 3 |
| ENSG00000196876 | SCN8A | -0.7890809 | 3 |
| ENSG00000170579 | DLGAP1 | -0.7792697 | 3 |
| ENSG00000005955 | GGNBP2 | -0.7656571 | 3 |
| ENSG00000132294 | EFR3A | -0.7504244 | 3 |
| ENSG00000078328 | RBFOX1 | -0.7315557 | 3 |
| ENSG00000003147 | ICA1 | -0.7117475 | 3 |
| ENSG00000197535 | MYO5A | -0.7116705 | 3 |
| ENSG00000175497 | DPP10 | -0.7061637 | 3 |
| ENSG00000182621 | PLCB1 | -0.6921795 | 3 |
| ENSG00000171759 | PAH | -0.6897098 | 3 |
| ENSG00000021645 | NRXN3 | -0.6713796 | 3 |
| ENSG00000166501 | PRKCB | -0.6155044 | 3 |
| ENSG00000183454 | GRIN2A | -0.5294268 | 3 |
| ENSG00000146830 | GIGYF1 | -0.5256914 | 3 |
| ENSG00000185345 | PARK2 | -0.4980108 | 3 |
| ENSG00000164506 | STXBP5 | -0.4858781 | 3 |
| ENSG00000132024 | CC2D1A | -0.4128981 | 3 |
| ENSG00000050030 | KIAA2022 | -0.4002974 | 3 |
| ENSG00000168116 | KIAA1586 | -0.3735057 | 3 |
| ENSG00000133026 | MYH10 | -0.3267293 | 3 |
| ENSG00000101489 | CELF4 | -0.3181586 | 3 |
| ENSG00000185008 | ROBO2 | -0.3077778 | 3 |
| ENSG00000182256 | GABRG3 | -0.2873575 | 3 |
| ENSG00000139174 | PRICKLE1 | -0.2147716 | 3 |
| ENSG00000138411 | HECW2 | -0.1373931 | 3 |
| ENSG00000140945 | CDH13 | 0.0845059 | 3 |
| ENSG00000166147 | FBN1 | -0.0824793 | 3 |
| ENSG00000171723 | GPHN | -0.0749364 | 3 |
| ENSG00000149972 | CNTN5 | -0.0485789 | 3 |
| ENSG00000144406 | UNC80 | -0.8620647 | 4 |
| ENSG00000135631 | RAB11FIP5 | -0.8335775 | 4 |
| ENSG00000132639 | SNAP25 | -0.8106671 | 4 |
| ENSG00000163618 | CADPS | -0.8086094 | 4 |
| ENSG00000159082 | SYNJ1 | -0.7858980 | 4 |
| ENSG00000104093 | DMXL2 | -0.7629031 | 4 |
| ENSG00000172260 | NEGR1 | -0.7083293 | 4 |
| ENSG00000163399 | ATP1A1 | -0.6896505 | 4 |
| ENSG00000115840 | SLC25A12 | -0.6828765 | 4 |
| ENSG00000196090 | PTPRT | -0.6742079 | 4 |
| ENSG00000130477 | UNC13A | -0.6667346 | 4 |
| ENSG00000154263 | ABCA10 | -0.6407178 | 4 |
| ENSG00000153575 | TUBGCP5 | -0.6283954 | 4 |
| ENSG00000136928 | GABBR2 | -0.6252262 | 4 |
| ENSG00000144290 | SLC4A10 | -0.5769256 | 4 |
| ENSG00000144331 | ZNF385B | -0.5383310 | 4 |
| ENSG00000186094 | AGBL4 | -0.5377404 | 4 |
| ENSG00000107105 | ELAVL2 | -0.5374477 | 4 |
| ENSG00000196433 | ASMT | -0.5323119 | 4 |
| ENSG00000155961 | RAB39B | -0.5256637 | 4 |
| ENSG00000116141 | MARK1 | -0.5062338 | 4 |
| ENSG00000155886 | SLC24A2 | -0.4946020 | 4 |
| ENSG00000109158 | GABRA4 | -0.4943945 | 4 |
| ENSG00000198010 | DLGAP2 | -0.4942182 | 4 |
| ENSG00000243232 | PCDHAC2 | -0.4777463 | 4 |
| ENSG00000128594 | LRRC4 | -0.4719222 | 4 |
| ENSG00000175161 | CADM2 | -0.4567470 | 4 |
| ENSG00000182901 | RGS7 | -0.4466930 | 4 |
| ENSG00000133019 | CHRM3 | -0.4273514 | 4 |
| ENSG00000172915 | NBEA | -0.4149285 | 4 |
| ENSG00000156113 | KCNMA1 | -0.4061200 | 4 |
| ENSG00000081189 | MEF2C | -0.4000890 | 4 |
| ENSG00000114374 | USP9Y | -0.3520447 | 4 |
| ENSG00000174442 | ZWILCH | -0.3276604 | 4 |
| ENSG00000136653 | RASSF5 | -0.3267914 | 4 |
| ENSG00000151790 | TDO2 | -0.3195670 | 4 |
| ENSG00000152495 | CAMK4 | -0.3178490 | 4 |
| ENSG00000060140 | STYK1 | -0.3136339 | 4 |
| ENSG00000064687 | ABCA7 | -0.3112037 | 4 |
| ENSG00000165355 | FBXO33 | -0.3050639 | 4 |
| ENSG00000135127 | CCDC64 | -0.3047331 | 4 |
| ENSG00000170289 | CNGB3 | -0.2731444 | 4 |
| ENSG00000139915 | MDGA2 | -0.2430880 | 4 |
| ENSG00000152402 | GUCY1A2 | -0.2327724 | 4 |
| ENSG00000111615 | KRR1 | -0.2248392 | 4 |
| ENSG00000100359 | SGSM3 | -0.2075939 | 4 |
| ENSG00000159640 | ACE | -0.1924407 | 4 |
| ENSG00000004468 | CD38 | -0.1674841 | 4 |
| ENSG00000128513 | POT1 | -0.1434731 | 4 |
| ENSG00000244588 | RAD21L1 | -0.1256607 | 4 |
| ENSG00000165379 | LRFN5 | -0.1218335 | 4 |
| ENSG00000100038 | TOP3B | -0.0993690 | 4 |
| ENSG00000152214 | RIT2 | 0.0792829 | 4 |
| ENSG00000165246 | NLGN4Y | -0.0675768 | 4 |
| ENSG00000181036 | FCRL6 | 0.0604928 | 4 |
| ENSG00000007168 | PAFAH1B1 | -0.8564947 | 5 |
| ENSG00000087470 | DNM1L | -0.8491182 | 5 |
| ENSG00000104725 | NEFL | -0.7912259 | 5 |
| ENSG00000022355 | GABRA1 | -0.7777200 | 5 |
| ENSG00000118596 | SLC16A7 | -0.7640017 | 5 |
| ENSG00000171735 | CAMTA1 | -0.7302306 | 5 |
| ENSG00000146469 | VIP | -0.6852620 | 5 |
| ENSG00000152969 | JAKMIP1 | -0.6814938 | 5 |
| ENSG00000106123 | EPHB6 | -0.6655863 | 5 |
| ENSG00000173726 | TOMM20 | -0.6303781 | 5 |
| ENSG00000100362 | PVALB | -0.4940183 | 5 |
| ENSG00000007171 | NOS2 | -0.4656878 | 5 |
| ENSG00000102468 | HTR2A | -0.4562790 | 5 |
| ENSG00000145979 | TBC1D7 | -0.4227883 | 5 |
| ENSG00000130707 | ASS1 | -0.3913746 | 5 |
| ENSG00000158258 | CLSTN2 | -0.3471046 | 5 |
| ENSG00000196730 | DAPK1 | -0.2855914 | 5 |
| ENSG00000166603 | MC4R | -0.2783239 | 5 |
| ENSG00000185666 | SYN3 | -0.2690820 | 5 |
| ENSG00000157168 | NRG1 | -0.1662121 | 5 |
| ENSG00000166866 | MYO1A | -0.1473913 | 5 |
| ENSG00000106113 | CRHR2 | -0.0441381 | 5 |
| ENSG00000077279 | DCX | -0.5750475 | 6 |
| ENSG00000148680 | HTR7 | -0.4261417 | 6 |
| ENSG00000125780 | TGM3 | -0.3834141 | 6 |
Modules with the strongest module-diagnosis correlation should have the highest percentage of SFARI Genes, but this doesn’t seem to be the case (even the opposite may be true)
plot_data = dataset %>% mutate('hasSFARIscore' = gene.score!='None') %>%
group_by(Module, MTcor, hasSFARIscore) %>% summarise(p=n()) %>%
left_join(dataset %>% group_by(Module) %>% summarise(n=n()), by='Module') %>%
mutate(p=round(p/n*100,2))
for(i in 1:nrow(plot_data)){
this_row = plot_data[i,]
if(this_row$hasSFARIscore==FALSE & this_row$p==100){
new_row = this_row
new_row$hasSFARIscore = TRUE
new_row$p = 0
plot_data = plot_data %>% rbind(new_row)
}
}
plot_data = plot_data %>% filter(hasSFARIscore==TRUE)
ggplotly(plot_data %>% ggplot(aes(MTcor, p, size=n)) + geom_smooth(color='gray', se=FALSE) +
geom_point(color=plot_data$Module, alpha=0.5, aes(id=Module)) + geom_hline(yintercept=mean(plot_data$p), color='gray') +
xlab('Module-Diagnosis correlation') + ylab('% of SFARI genes') +
theme_minimal() + theme(legend.position = 'none'))
rm(i, this_row, new_row, plot_data)
Since these modules have the strongest relation to autism, this pattern should be reflected in their model eigengenes, having two different behaviours for the samples corresponding to autism and the ones corresponding to control.
In both cases, the Eigengenes separate the behaviour between autism and control samples very clearly!
plot_EGs = function(module){
plot_data = data.frame('ID' = rownames(MEs), 'MEs' = MEs[,paste0('ME',module)], 'Diagnosis' = datMeta$Diagnosis)
p = plot_data %>% ggplot(aes(Diagnosis, MEs, fill=Diagnosis)) + geom_boxplot() + theme_minimal() + theme(legend.position='none') +
ggtitle(paste0('Module ', module, ' (MTcor=',round(moduleTraitCor[paste0('ME',module),1],2),')'))
return(p)
}
p1 = plot_EGs(top_modules[1])
p2 = plot_EGs(top_modules[2])
grid.arrange(p1, p2, nrow=1)
rm(plot_EGs, p1, p2)
Selecting the modules with the highest correlation to Diagnosis, and, from them, the genes with the highest module membership-(absolute) gene significance
*Ordered by \(\frac{MM+|GS|}{2}\)
There aren’t that many SFARI genes in the top genes of the modules and not a single one belonging to scores 1 and 2
create_table = function(module){
top_genes = dataset %>% left_join(genes_info %>% dplyr::select(ID, external_gene_id), by='ID') %>%
dplyr::select(ID, external_gene_id, paste0('MM.',gsub('#','',module)), GS, gene.score) %>%
filter(dataset$Module==module) %>% dplyr::rename('MM' = paste0('MM.',gsub('#','',module))) %>%
mutate(importance = (MM+abs(GS))/2) %>% arrange(by=-importance) %>% top_n(20)
return(top_genes)
}
top_genes_1 = create_table(top_modules[1])
kable(top_genes_1, caption=paste0('Top 10 genes for module ', top_modules[1], ' (MTcor = ',
round(moduleTraitCor[paste0('ME',top_modules[1]),1],2),')'))
| ID | external_gene_id | MM | GS | gene.score | importance |
|---|---|---|---|---|---|
| ENSG00000143384 | MCL1 | 0.8476929 | 0.9075109 | None | 0.8776019 |
| ENSG00000161638 | ITGA5 | 0.8510043 | 0.8600246 | None | 0.8555144 |
| ENSG00000089159 | PXN | 0.8085675 | 0.8694821 | None | 0.8390248 |
| ENSG00000196935 | SRGAP1 | 0.7501468 | 0.9223277 | None | 0.8362373 |
| ENSG00000003402 | CFLAR | 0.7941123 | 0.8169199 | None | 0.8055161 |
| ENSG00000148841 | ITPRIP | 0.7881631 | 0.8162584 | None | 0.8022107 |
| ENSG00000124782 | RREB1 | 0.7589325 | 0.8451372 | None | 0.8020348 |
| ENSG00000106366 | SERPINE1 | 0.8065142 | 0.7907461 | 4 | 0.7986301 |
| ENSG00000150457 | LATS2 | 0.7443403 | 0.8501398 | None | 0.7972400 |
| ENSG00000138119 | MYOF | 0.7514562 | 0.8343142 | None | 0.7928852 |
| ENSG00000073792 | IGF2BP2 | 0.7392534 | 0.8389676 | None | 0.7891105 |
| ENSG00000138166 | DUSP5 | 0.8112245 | 0.7636771 | None | 0.7874508 |
| ENSG00000072364 | AFF4 | 0.7963444 | 0.7711840 | 6 | 0.7837642 |
| ENSG00000120278 | PLEKHG1 | 0.7556018 | 0.8087123 | None | 0.7821570 |
| ENSG00000162745 | OLFML2B | 0.7023713 | 0.8526646 | None | 0.7775179 |
| ENSG00000154640 | BTG3 | 0.7244112 | 0.8301676 | None | 0.7772894 |
| ENSG00000133639 | BTG1 | 0.7423866 | 0.8079258 | None | 0.7751562 |
| ENSG00000168769 | TET2 | 0.7312714 | 0.8178650 | 3 | 0.7745682 |
| ENSG00000120690 | ELF1 | 0.7613055 | 0.7876526 | None | 0.7744790 |
| ENSG00000158615 | PPP1R15B | 0.7289451 | 0.8197298 | None | 0.7743375 |
top_genes_2 = create_table(top_modules[2])
kable(top_genes_2, caption=paste0('Top 10 genes for module ', top_modules[2], ' (MTcor = ',
round(moduleTraitCor[paste0('ME',top_modules[2]),1],2),')'))
| ID | external_gene_id | MM | GS | gene.score | importance |
|---|---|---|---|---|---|
| ENSG00000050748 | MAPK9 | 0.9055266 | -0.9401065 | None | 0.9228165 |
| ENSG00000108395 | TRIM37 | 0.9056872 | -0.9297206 | None | 0.9177039 |
| ENSG00000138078 | PREPL | 0.8926949 | -0.9043456 | None | 0.8985203 |
| ENSG00000177432 | NAP1L5 | 0.8738603 | -0.9048896 | None | 0.8893750 |
| ENSG00000163577 | EIF5A2 | 0.8622485 | -0.9110526 | None | 0.8866505 |
| ENSG00000128881 | TTBK2 | 0.8938083 | -0.8754731 | None | 0.8846407 |
| ENSG00000176490 | DIRAS1 | 0.8610032 | -0.8968398 | None | 0.8789215 |
| ENSG00000171132 | PRKCE | 0.8477771 | -0.9090963 | None | 0.8784367 |
| ENSG00000155097 | ATP6V1C1 | 0.8504461 | -0.8966833 | None | 0.8735647 |
| ENSG00000196876 | SCN8A | 0.9470763 | -0.7890809 | 3 | 0.8680786 |
| ENSG00000114573 | ATP6V1A | 0.8139207 | -0.9175213 | None | 0.8657210 |
| ENSG00000111674 | ENO2 | 0.8761932 | -0.8485455 | None | 0.8623694 |
| ENSG00000125814 | NAPB | 0.8922318 | -0.8313011 | None | 0.8617664 |
| ENSG00000184368 | MAP7D2 | 0.8332534 | -0.8807598 | None | 0.8570066 |
| ENSG00000131437 | KIF3A | 0.8671455 | -0.8464587 | None | 0.8568021 |
| ENSG00000144285 | SCN1A | 0.9127631 | -0.8004590 | 3 | 0.8566110 |
| ENSG00000162694 | EXTL2 | 0.8023344 | -0.8984415 | None | 0.8503880 |
| ENSG00000172348 | RCAN2 | 0.7828069 | -0.9152892 | None | 0.8490480 |
| ENSG00000130540 | SULT4A1 | 0.8830724 | -0.8116827 | None | 0.8473775 |
| ENSG00000132639 | SNAP25 | 0.8784733 | -0.8106671 | 4 | 0.8445702 |
rm(create_table)
pca = datExpr %>% prcomp
plot_data = data.frame('ID'=rownames(datExpr), 'PC1' = pca$x[,1], 'PC2' = pca$x[,2]) %>%
left_join(dataset, by='ID') %>% dplyr::select(ID, PC1, PC2, Module, gene.score) %>%
mutate(color = ifelse(Module %in% top_modules, as.character(Module), 'gray')) %>%
mutate(alpha = ifelse(color %in% top_modules &
ID %in% c(as.character(top_genes_1$ID),
as.character(top_genes_2$ID)), 1, 0.1))
plot_data %>% ggplot(aes(PC1, PC2)) + geom_point(alpha=plot_data$alpha, color=plot_data$color) +
theme_minimal() + ggtitle('Important genes identified through WGCNA')
Using the package anRichment
It was designed by Peter Langfelder explicitly to perform enrichmen analysis on WGCNA’s modules in brain-related experiments (mainly Huntington’s Disease)
It has packages with brain annotations:
BrainDiseaseCollection: A Brain Disease Gene Set Collection for anRichment
MillerAIBSCollection: (included in anRichment) Contains gene sets collected by Jeremy A. Miller at AIBS of various cell type and brain region marker sets, gene sets collected from expression studies of developing brain, as well as a collection of transcription factor (TF) targets from the original ChEA study
The tutorial says it’s an experimental package
It’s not on CRAN nor in Bioconductor
# Prepare dataset
# Create dataset with top modules membership and removing the genes without an assigned module
EA_dataset = data.frame('ensembl_gene_id' = genes_info$ID,
module = ifelse(genes_info$Module %in% top_modules, genes_info$Module, 'other')) %>%
filter(genes_info$Module!='gray')
# Assign Entrez Gene Id to each gene
getinfo = c('ensembl_gene_id','entrezgene')
mart = useMart(biomart='ENSEMBL_MART_ENSEMBL', dataset='hsapiens_gene_ensembl', host='feb2014.archive.ensembl.org')
biomart_output = getBM(attributes=getinfo, filters=c('ensembl_gene_id'), values=EA_dataset$ensembl_gene_id, mart=mart)
## Cache found
EA_dataset = EA_dataset %>% left_join(biomart_output, by='ensembl_gene_id')
for(tm in top_modules){
cat(paste0('\n',sum(EA_dataset$module==tm & is.na(EA_dataset$entrezgene)), ' genes from top module ',
tm, ' don\'t have an Entrez Gene ID'))
}
##
## 8 genes from top module #F464E5 don't have an Entrez Gene ID
## 29 genes from top module #00BECA don't have an Entrez Gene ID
rm(getinfo, mart, biomart_output, tm)
# Manual: https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/GeneAnnotation/Tutorials/anRichment-Tutorial1.pdf
collectGarbage()
# EA_dataset = rbind(EA_dataset[EA_dataset$module!='other',], EA_dataset[EA_dataset$module=='other',][sample(sum(EA_dataset$module=='other'), 1000),])
# Prepare datasets
GO_col = buildGOcollection(organism = 'human', verbose = 0)
## Loading required package: org.Hs.eg.db
##
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
internal_col = internalCollection(organism = 'human')
MillerAIBS_col = MillerAIBSCollection(organism = 'human')
BrainDisease_col = BrainDiseaseCollection(organism = 'human')
combined_col = mergeCollections(GO_col, internal_col, MillerAIBS_col, BrainDisease_col)
# Print collections used
cat('Using collections: ')
## Using collections:
knownGroups(combined_col, sortBy = 'size')
## [1] "GO"
## [2] "GO.BP"
## [3] "GO.MF"
## [4] "GO.CC"
## [5] "JA Miller at AIBS"
## [6] "Chip-X enrichment analysis (ChEA)"
## [7] "Brain"
## [8] "JAM"
## [9] "Prenatal brain"
## [10] "Brain region markers"
## [11] "Cortex"
## [12] "Brain region marker enriched gene sets"
## [13] "WGCNA"
## [14] "BrainRegionMarkers"
## [15] "BrainRegionMarkers.HBA"
## [16] "BrainRegionMarkers.HBA.localMarker(top200)"
## [17] "Postnatal brain"
## [18] "ImmunePathways"
## [19] "Markers of cortex layers"
## [20] "BrainLists"
## [21] "Cell type markers"
## [22] "Germinal brain"
## [23] "BrainRegionMarkers.HBA.globalMarker(top200)"
## [24] "Accelerated evolution"
## [25] "Postmitotic brain"
## [26] "BrainLists.Blalock_AD"
## [27] "BrainLists.DiseaseGenes"
## [28] "BloodAtlases"
## [29] "Verge Disease Genes"
## [30] "BloodAtlases.Whitney"
## [31] "BrainLists.JAXdiseaseGene"
## [32] "BrainLists.MO"
## [33] "Age-associated genes"
## [34] "BrainLists.Lu_Aging"
## [35] "Cell type marker enriched gene sets"
## [36] "BrainLists.CA1vsCA3"
## [37] "BrainLists.MitochondrialType"
## [38] "BrainLists.MO.2+_26Mar08"
## [39] "BrainLists.MO.Sugino"
## [40] "BloodAtlases.Gnatenko2"
## [41] "BloodAtlases.Kabanova"
## [42] "BrainLists.Voineagu"
## [43] "StemCellLists"
## [44] "StemCellLists.Lee"
# Perform Enrichment Analysis
enrichment = enrichmentAnalysis(classLabels = EA_dataset$module, identifiers = EA_dataset$entrezgene,
refCollection = combined_col, useBackground = 'given',
threshold = 1e-4, thresholdType = 'Bonferroni',
getOverlapEntrez = FALSE, getOverlapSymbols = TRUE)
## enrichmentAnalysis: preparing data..
## ..working on label set 1 ..
kable(enrichment$enrichmentTable %>% filter(class==top_modules[1]) %>%
dplyr::select(dataSetID, shortDataSetName, inGroups, Bonferroni, FDR,
effectiveClassSize, effectiveSetSize, nCommonGenes),
caption = paste0('Enriched terms for module ', top_modules[1], ' (MTcor = ',
round(genes_info$MTcor[genes_info$Module==top_modules[1]][1],4), ')'))
| dataSetID | shortDataSetName | inGroups | Bonferroni | FDR | effectiveClassSize | effectiveSetSize | nCommonGenes |
|---|---|---|---|---|---|---|---|
| JAMiller.AIBS.000434 | Genes bound by RELA in HUMAN FIBROSARCOMA from PMID 24523406 | JA Miller at AIBS|Chip-X enrichment analysis (ChEA) | 0.0000002 | 0.0e+00 | 642 | 1043 | 89 |
| GO:0001525 | angiogenesis | GO|GO.BP | 0.0000071 | 1.0e-07 | 642 | 434 | 48 |
| GO:0072358 | cardiovascular system development | GO|GO.BP | 0.0000273 | 3.0e-07 | 642 | 624 | 59 |
| GO:0007166 | cell surface receptor signaling pathway | GO|GO.BP | 0.0000365 | 3.0e-07 | 642 | 2498 | 157 |
| GO:0001944 | vasculature development | GO|GO.BP | 0.0000429 | 4.0e-07 | 642 | 615 | 58 |
| GO:0072359 | circulatory system development | GO|GO.BP | 0.0000488 | 4.0e-07 | 642 | 941 | 77 |
| GO:0001568 | blood vessel development | GO|GO.BP | 0.0001549 | 1.3e-06 | 642 | 587 | 55 |
| GO:0048514 | blood vessel morphogenesis | GO|GO.BP | 0.0002049 | 1.6e-06 | 642 | 511 | 50 |
| GO:0035295 | tube development | GO|GO.BP | 0.0005272 | 3.7e-06 | 642 | 884 | 71 |
| GO:0030198 | extracellular matrix organization | GO|GO.BP | 0.0007586 | 5.1e-06 | 642 | 316 | 36 |
kable(enrichment$enrichmentTable %>% filter(class==top_modules[2]) %>%
dplyr::select(dataSetID, shortDataSetName, inGroups, Bonferroni, FDR,
effectiveClassSize, effectiveSetSize, nCommonGenes),
caption = paste0('Enriched terms for module ', top_modules[2], ' (MTcor = ',
round(genes_info$MTcor[genes_info$Module==top_modules[2]][1],4), ')'))
| dataSetID | shortDataSetName | inGroups | Bonferroni | FDR | effectiveClassSize | effectiveSetSize | nCommonGenes |
|---|---|---|---|---|---|---|---|
| JAMiller.AIBS.000142 | Highest in CP of 13-16 post-conception weeks human | JA Miller at AIBS|Brain|Prenatal brain|Markers of cortex layers|Cortex|Postmitotic brain | 0.00e+00 | 0e+00 | 1637 | 1218 | 301 |
| JAMiller.AIBS.000052 | CortexWGCNA 15-21 post-conception weeks C26 | JA Miller at AIBS|Brain|Prenatal brain|Cortex|WGCNA | 0.00e+00 | 0e+00 | 1637 | 726 | 189 |
| JAMiller.AIBS.000150 | Highest in CP of E14.5 mouse | JA Miller at AIBS|Brain|Prenatal brain|Markers of cortex layers|Cortex|Postmitotic brain | 0.00e+00 | 0e+00 | 1637 | 1276 | 273 |
| JAMiller.AIBS.000155 | Lowest in VZ of E14.5 mouse | JA Miller at AIBS|Brain|Prenatal brain|Markers of cortex layers|Cortex | 0.00e+00 | 0e+00 | 1637 | 1674 | 317 |
| JAMiller.AIBS.000569 | WGCNA humanSpecificOlivedrab2Module frontalCtx FOXP2 | JA Miller at AIBS|Brain|Postnatal brain|Cortex|WGCNA | 0.00e+00 | 0e+00 | 1637 | 4057 | 599 |
| JAM:002744 | Autism_differential_expression_across_at_least_one_comparison | JAM|BrainLists|BrainLists.Voineagu | 0.00e+00 | 0e+00 | 1637 | 765 | 175 |
| JAM:003016 | downAD_synapticTransmission | JAM|BrainLists|BrainLists.Blalock_AD | 0.00e+00 | 0e+00 | 1637 | 89 | 48 |
| JAMiller.AIBS.000141 | CP enriched in E14.5 mouse | JA Miller at AIBS|Brain|Prenatal brain|Markers of cortex layers|Cortex|Postmitotic brain | 0.00e+00 | 0e+00 | 1637 | 569 | 139 |
| JAMiller.AIBS.000506 | Genes bound by SUZ12 in MOUSE MESC from PMID 20075857 | JA Miller at AIBS|Chip-X enrichment analysis (ChEA) | 0.00e+00 | 0e+00 | 1637 | 3380 | 497 |
| GO:0097458 | neuron part | GO|GO.CC | 0.00e+00 | 0e+00 | 1637 | 1607 | 281 |
| JAMiller.AIBS.000570 | WGCNA Olivedrab2ModuleGenes with enriched ELAVL2 targets | JA Miller at AIBS|Brain|Postnatal brain|Cortex|WGCNA | 0.00e+00 | 0e+00 | 1637 | 483 | 119 |
| GO:0045202 | synapse | GO|GO.CC | 0.00e+00 | 0e+00 | 1637 | 1114 | 208 |
| GO:0099536 | synaptic signaling | GO|GO.BP | 0.00e+00 | 0e+00 | 1637 | 652 | 142 |
| GO:0044456 | synapse part | GO|GO.CC | 0.00e+00 | 0e+00 | 1637 | 893 | 175 |
| GO:0007268 | chemical synaptic transmission | GO|GO.BP | 0.00e+00 | 0e+00 | 1637 | 638 | 138 |
| GO:0098916 | anterograde trans-synaptic signaling | GO|GO.BP | 0.00e+00 | 0e+00 | 1637 | 638 | 138 |
| GO:0099537 | trans-synaptic signaling | GO|GO.BP | 0.00e+00 | 0e+00 | 1637 | 646 | 139 |
| JAMiller.AIBS.000005 | CPi markers at 21 post-conception weeks | JA Miller at AIBS|Brain|Prenatal brain|Cortex|Markers of cortex layers|Postmitotic brain | 0.00e+00 | 0e+00 | 1637 | 312 | 85 |
| JAM:003072 | Tail of Caudate Nucleus_IN_Striatum | JAM|BrainRegionMarkers|BrainRegionMarkers.HBA|BrainRegionMarkers.HBA.localMarker(top200)|Brain region markers|Brain region marker enriched gene sets | 0.00e+00 | 0e+00 | 1637 | 162 | 57 |
| JAM:002805 | Cerebral Cortex | JAM|BrainRegionMarkers|BrainRegionMarkers.HBA|BrainRegionMarkers.HBA.globalMarker(top200)|Brain region markers|Brain region marker enriched gene sets | 0.00e+00 | 0e+00 | 1637 | 161 | 56 |
| JAMiller.AIBS.000149 | Lowest in VZ of 13-16 post-conception weeks human | JA Miller at AIBS|Brain|Prenatal brain|Markers of cortex layers|Cortex | 0.00e+00 | 0e+00 | 1637 | 618 | 131 |
| JAMiller.AIBS.000123 | HippocampusWGCNA turquoise DGenriched upAge | JA Miller at AIBS|Brain|Postnatal brain|WGCNA | 0.00e+00 | 0e+00 | 1637 | 1104 | 198 |
| JAM:002751 | Basal Pons | JAM|BrainRegionMarkers|BrainRegionMarkers.HBA|BrainRegionMarkers.HBA.globalMarker(top200)|Brain region markers|Brain region marker enriched gene sets | 0.00e+00 | 0e+00 | 1637 | 167 | 56 |
| GO:0043005 | neuron projection | GO|GO.CC | 0.00e+00 | 0e+00 | 1637 | 1214 | 210 |
| JAM:003054 | subiculum_IN_Hippocampal Formation | JAM|BrainRegionMarkers|BrainRegionMarkers.HBA|BrainRegionMarkers.HBA.localMarker(top200)|Brain region markers|Brain region marker enriched gene sets | 0.00e+00 | 0e+00 | 1637 | 164 | 53 |
| JAM:002739 | arcuate nucleus of medulla_IN_Myelencephalon | JAM|BrainRegionMarkers|BrainRegionMarkers.HBA|BrainRegionMarkers.HBA.localMarker(top200)|Brain region markers|Brain region marker enriched gene sets | 0.00e+00 | 0e+00 | 1637 | 168 | 53 |
| GO:0097060 | synaptic membrane | GO|GO.CC | 0.00e+00 | 0e+00 | 1637 | 412 | 94 |
| JAM:002907 | inferior olivary complex_IN_Myelencephalon | JAM|BrainRegionMarkers|BrainRegionMarkers.HBA|BrainRegionMarkers.HBA.localMarker(top200)|Brain region markers|Brain region marker enriched gene sets | 0.00e+00 | 0e+00 | 1637 | 170 | 53 |
| JAMiller.AIBS.000504 | Genes bound by SUZ12 in mouse MESC from PMID 18692474 | JA Miller at AIBS|Chip-X enrichment analysis (ChEA) | 0.00e+00 | 0e+00 | 1637 | 1362 | 222 |
| JAM:002824 | Dentate Nucleus_IN_Cerebellar Nucleus | JAM|BrainRegionMarkers|BrainRegionMarkers.HBA|BrainRegionMarkers.HBA.localMarker(top200)|Brain region markers|Brain region marker enriched gene sets | 0.00e+00 | 0e+00 | 1637 | 165 | 51 |
| GO:0030424 | axon | GO|GO.CC | 0.00e+00 | 0e+00 | 1637 | 574 | 116 |
| GO:1902495 | transmembrane transporter complex | GO|GO.CC | 0.00e+00 | 0e+00 | 1637 | 289 | 72 |
| JAM:003058 | Substantia Nigra, pars compacta_IN_Mesencephalon | JAM|BrainRegionMarkers|BrainRegionMarkers.HBA|BrainRegionMarkers.HBA.localMarker(top200)|Brain region markers|Brain region marker enriched gene sets | 0.00e+00 | 0e+00 | 1637 | 169 | 51 |
| GO:0034702 | ion channel complex | GO|GO.CC | 0.00e+00 | 0e+00 | 1637 | 269 | 68 |
| JAMiller.AIBS.000364 | Genes bound by MTF2 in MOUSE MESC from PMID 20144788 | JA Miller at AIBS|Chip-X enrichment analysis (ChEA) | 0.00e+00 | 0e+00 | 1637 | 2292 | 330 |
| JAM:002918 | lateral medullary reticular group_IN_Myelencephalon | JAM|BrainRegionMarkers|BrainRegionMarkers.HBA|BrainRegionMarkers.HBA.localMarker(top200)|Brain region markers|Brain region marker enriched gene sets | 0.00e+00 | 0e+00 | 1637 | 162 | 49 |
| GO:1990351 | transporter complex | GO|GO.CC | 1.00e-07 | 0e+00 | 1637 | 297 | 72 |
| JAM:002764 | downAging_mitochondria_synapse | JAM|BrainLists|BrainLists.Lu_Aging | 1.00e-07 | 0e+00 | 1637 | 391 | 86 |
| GO:0045211 | postsynaptic membrane | GO|GO.CC | 1.00e-07 | 0e+00 | 1637 | 309 | 73 |
| JAMiller.AIBS.000503 | Genes bound by SUZ12 in mouse MESC from PMID 18555785 | JA Miller at AIBS|Chip-X enrichment analysis (ChEA) | 2.00e-07 | 0e+00 | 1637 | 765 | 138 |
| JAM:002991 | downAD_synapticTransmission | JAM|BrainLists|BrainLists.Blalock_AD | 3.00e-07 | 0e+00 | 1637 | 97 | 35 |
| JAM:002967 | Occipital Lobe_IN_Cerebral Cortex | JAM|BrainRegionMarkers|BrainRegionMarkers.HBA|BrainRegionMarkers.HBA.localMarker(top200)|Brain region markers|Brain region marker enriched gene sets | 4.00e-07 | 0e+00 | 1637 | 149 | 45 |
| GO:0050877 | nervous system process | GO|GO.BP | 5.00e-07 | 0e+00 | 1637 | 886 | 153 |
| JAM:002964 | Nucleus Accumbens_IN_Striatum | JAM|BrainRegionMarkers|BrainRegionMarkers.HBA|BrainRegionMarkers.HBA.localMarker(top200)|Brain region markers|Brain region marker enriched gene sets | 6.00e-07 | 0e+00 | 1637 | 161 | 47 |
| GO:0098794 | postsynapse | GO|GO.CC | 6.00e-07 | 0e+00 | 1637 | 591 | 113 |
| GO:0120025 | plasma membrane bounded cell projection | GO|GO.CC | 8.00e-07 | 0e+00 | 1637 | 1919 | 280 |
| GO:0034703 | cation channel complex | GO|GO.CC | 9.00e-07 | 0e+00 | 1637 | 203 | 54 |
| JAM:002882 | Hippocampal Formation | JAM|BrainRegionMarkers|BrainRegionMarkers.HBA|BrainRegionMarkers.HBA.globalMarker(top200)|Brain region markers|Brain region marker enriched gene sets | 1.00e-06 | 0e+00 | 1637 | 169 | 48 |
| JAM:002763 | downAD_metalIonTransport_glycoprotein | JAM|BrainLists|BrainLists.Blalock_AD | 1.10e-06 | 0e+00 | 1637 | 283 | 67 |
| JAM:003085 | trochlear nucleus_IN_Mesencephalon | JAM|BrainRegionMarkers|BrainRegionMarkers.HBA|BrainRegionMarkers.HBA.localMarker(top200)|Brain region markers|Brain region marker enriched gene sets | 1.20e-06 | 0e+00 | 1637 | 153 | 45 |
| GO:0007267 | cell-cell signaling | GO|GO.BP | 1.60e-06 | 0e+00 | 1637 | 1439 | 221 |
| GO:0042995 | cell projection | GO|GO.CC | 2.60e-06 | 0e+00 | 1637 | 1989 | 286 |
| GO:0050804 | modulation of chemical synaptic transmission | GO|GO.BP | 4.40e-06 | 1e-07 | 1637 | 411 | 85 |
| GO:0005886 | plasma membrane | GO|GO.CC | 4.50e-06 | 1e-07 | 1637 | 4277 | 541 |
| GO:0099177 | regulation of trans-synaptic signaling | GO|GO.BP | 5.00e-06 | 1e-07 | 1637 | 412 | 85 |
| GO:0098793 | presynapse | GO|GO.CC | 6.10e-06 | 1e-07 | 1637 | 462 | 92 |
| GO:0071944 | cell periphery | GO|GO.CC | 7.10e-06 | 1e-07 | 1637 | 4381 | 551 |
| GO:0098978 | glutamatergic synapse | GO|GO.CC | 7.70e-06 | 1e-07 | 1637 | 341 | 74 |
| JAM:002920 | Lateral Nucleus_IN_Amygdala | JAM|BrainRegionMarkers|BrainRegionMarkers.HBA|BrainRegionMarkers.HBA.localMarker(top200)|Brain region markers|Brain region marker enriched gene sets | 8.40e-06 | 1e-07 | 1637 | 167 | 46 |
| GO:0005216 | ion channel activity | GO|GO.MF | 8.80e-06 | 1e-07 | 1637 | 362 | 77 |
| JAM:002769 | downAD_mitochondrion | JAM|BrainLists|BrainLists.Blalock_AD | 1.04e-05 | 1e-07 | 1637 | 265 | 62 |
| JAMiller.AIBS.000463 | Genes bound by SMAD4 in HUMAN A2780 from PMID 21799915 | JA Miller at AIBS|Chip-X enrichment analysis (ChEA) | 1.18e-05 | 1e-07 | 1637 | 2085 | 294 |
| JAMiller.AIBS.000042 | CortexWGCNA 15-21 post-conception weeks C16 SPenriched | JA Miller at AIBS|Brain|Prenatal brain|Cortex|WGCNA | 1.21e-05 | 1e-07 | 1637 | 198 | 51 |
| GO:0036477 | somatodendritic compartment | GO|GO.CC | 1.63e-05 | 2e-07 | 1637 | 795 | 136 |
| GO:0044463 | cell projection part | GO|GO.CC | 1.91e-05 | 2e-07 | 1637 | 1345 | 205 |
| GO:0120038 | plasma membrane bounded cell projection part | GO|GO.CC | 1.91e-05 | 2e-07 | 1637 | 1345 | 205 |
| GO:0006812 | cation transport | GO|GO.BP | 1.93e-05 | 2e-07 | 1637 | 1007 | 163 |
| GO:0005261 | cation channel activity | GO|GO.MF | 2.04e-05 | 2e-07 | 1637 | 282 | 64 |
| GO:0022803 | passive transmembrane transporter activity | GO|GO.MF | 2.14e-05 | 2e-07 | 1637 | 389 | 80 |
| GO:0022838 | substrate-specific channel activity | GO|GO.MF | 2.96e-05 | 3e-07 | 1637 | 371 | 77 |
| GO:0003008 | system process | GO|GO.BP | 3.92e-05 | 4e-07 | 1637 | 1505 | 223 |
| JAM:002866 | GlutamatergicNeuronsInMouseCortex_Sugino | JAM|BrainLists|BrainLists.MO|BrainLists.MO.Sugino|Cell type marker enriched gene sets|Brain region marker enriched gene sets | 4.29e-05 | 4e-07 | 1637 | 367 | 76 |
| GO:0015267 | channel activity | GO|GO.MF | 4.53e-05 | 4e-07 | 1637 | 388 | 79 |
| JAMiller.AIBS.000436 | Genes bound by REST in MOUSE MESC from PMID 21632747 | JA Miller at AIBS|Chip-X enrichment analysis (ChEA) | 4.54e-05 | 4e-07 | 1637 | 1675 | 243 |
| JAMiller.AIBS.000505 | Genes bound by SUZ12 in MOUSE MESC from PMID 18974828 | JA Miller at AIBS|Chip-X enrichment analysis (ChEA) | 5.82e-05 | 5e-07 | 1637 | 1387 | 208 |
| GO:0043269 | regulation of ion transport | GO|GO.BP | 5.94e-05 | 5e-07 | 1637 | 613 | 110 |
Save Enrichment Analysis results
save(enrichment, file='./../Data/enrichmentAnalysis.RData')
sessionInfo()
## R version 3.6.0 (2019-04-26)
## Platform: x86_64-redhat-linux-gnu (64-bit)
## Running under: Scientific Linux 7.6 (Nitrogen)
##
## Matrix products: default
## BLAS/LAPACK: /usr/lib64/R/lib/libRblas.so
##
## locale:
## [1] LC_CTYPE=en_GB.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB.UTF-8 LC_COLLATE=en_GB.UTF-8
## [5] LC_MONETARY=en_GB.UTF-8 LC_MESSAGES=en_GB.UTF-8
## [7] LC_PAPER=en_GB.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats4 parallel stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] org.Hs.eg.db_3.10.0
## [2] BrainDiseaseCollection_1.00
## [3] anRichment_1.01-2
## [4] TxDb.Mmusculus.UCSC.mm10.knownGene_3.10.0
## [5] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
## [6] GenomicFeatures_1.38.2
## [7] GenomicRanges_1.38.0
## [8] GenomeInfoDb_1.22.0
## [9] anRichmentMethods_0.90-1
## [10] WGCNA_1.68
## [11] fastcluster_1.1.25
## [12] dynamicTreeCut_1.63-1
## [13] GO.db_3.10.0
## [14] AnnotationDbi_1.48.0
## [15] IRanges_2.20.2
## [16] S4Vectors_0.24.3
## [17] Biobase_2.46.0
## [18] BiocGenerics_0.32.0
## [19] biomaRt_2.42.0
## [20] knitr_1.24
## [21] doParallel_1.0.15
## [22] iterators_1.0.12
## [23] foreach_1.4.7
## [24] polycor_0.7-10
## [25] expss_0.10.1
## [26] GGally_1.4.0
## [27] gridExtra_2.3
## [28] viridis_0.5.1
## [29] viridisLite_0.3.0
## [30] RColorBrewer_1.1-2
## [31] dendextend_1.13.3
## [32] plotly_4.9.2
## [33] glue_1.3.1
## [34] reshape2_1.4.3
## [35] forcats_0.4.0
## [36] stringr_1.4.0
## [37] dplyr_0.8.3
## [38] purrr_0.3.3
## [39] readr_1.3.1
## [40] tidyr_1.0.2
## [41] tibble_2.1.3
## [42] ggplot2_3.2.1
## [43] tidyverse_1.3.0
##
## loaded via a namespace (and not attached):
## [1] readxl_1.3.1 backports_1.1.5
## [3] Hmisc_4.2-0 BiocFileCache_1.10.2
## [5] plyr_1.8.5 lazyeval_0.2.2
## [7] splines_3.6.0 crosstalk_1.0.0
## [9] BiocParallel_1.20.1 robust_0.4-18.2
## [11] digest_0.6.24 htmltools_0.4.0
## [13] fansi_0.4.1 magrittr_1.5
## [15] checkmate_1.9.4 memoise_1.1.0
## [17] fit.models_0.5-14 cluster_2.0.8
## [19] annotate_1.64.0 Biostrings_2.54.0
## [21] modelr_0.1.5 matrixStats_0.55.0
## [23] askpass_1.1 prettyunits_1.0.2
## [25] colorspace_1.4-1 blob_1.2.1
## [27] rvest_0.3.5 rappdirs_0.3.1
## [29] rrcov_1.4-7 haven_2.2.0
## [31] xfun_0.8 crayon_1.3.4
## [33] RCurl_1.95-4.12 jsonlite_1.6
## [35] genefilter_1.68.0 impute_1.60.0
## [37] survival_2.44-1.1 gtable_0.3.0
## [39] zlibbioc_1.32.0 XVector_0.26.0
## [41] DelayedArray_0.12.2 DEoptimR_1.0-8
## [43] scales_1.1.0 mvtnorm_1.0-11
## [45] DBI_1.1.0 Rcpp_1.0.3
## [47] xtable_1.8-4 progress_1.2.2
## [49] htmlTable_1.13.1 foreign_0.8-71
## [51] bit_1.1-15.2 preprocessCore_1.48.0
## [53] Formula_1.2-3 htmlwidgets_1.5.1
## [55] httr_1.4.1 ellipsis_0.3.0
## [57] acepack_1.4.1 farver_2.0.3
## [59] pkgconfig_2.0.3 reshape_0.8.8
## [61] XML_3.99-0.3 nnet_7.3-12
## [63] dbplyr_1.4.2 locfit_1.5-9.1
## [65] later_1.0.0 labeling_0.3
## [67] tidyselect_0.2.5 rlang_0.4.4
## [69] munsell_0.5.0 cellranger_1.1.0
## [71] tools_3.6.0 cli_2.0.1
## [73] generics_0.0.2 RSQLite_2.2.0
## [75] broom_0.5.4 fastmap_1.0.1
## [77] evaluate_0.14 yaml_2.2.0
## [79] bit64_0.9-7 fs_1.3.1
## [81] robustbase_0.93-5 nlme_3.1-139
## [83] mime_0.9 xml2_1.2.2
## [85] compiler_3.6.0 rstudioapi_0.10
## [87] curl_4.3 reprex_0.3.0
## [89] geneplotter_1.64.0 pcaPP_1.9-73
## [91] stringi_1.4.6 highr_0.8
## [93] lattice_0.20-38 Matrix_1.2-17
## [95] vctrs_0.2.2 pillar_1.4.3
## [97] lifecycle_0.1.0 data.table_1.12.8
## [99] bitops_1.0-6 httpuv_1.5.2
## [101] rtracklayer_1.46.0 R6_2.4.1
## [103] latticeExtra_0.6-28 promises_1.1.0
## [105] codetools_0.2-16 MASS_7.3-51.4
## [107] assertthat_0.2.1 SummarizedExperiment_1.16.1
## [109] DESeq2_1.26.0 openssl_1.4.1
## [111] withr_2.1.2 GenomicAlignments_1.22.1
## [113] Rsamtools_2.2.2 GenomeInfoDbData_1.2.2
## [115] hms_0.5.3 grid_3.6.0
## [117] rpart_4.1-15 rmarkdown_1.14
## [119] Cairo_1.5-10 shiny_1.4.0
## [121] lubridate_1.7.4 base64enc_0.1-3